Contributors: Aymn Elhaddad (GRO INTELLIGENCE), Stephan Meyer zum Alten Borgloh (GRO INTELLIGENCE), Allard de Wit (WAGENINGEN ENVIRONMENTAL RESEARCH)
Issued by: WEnR and GRO
Issued Date: April 2019
Ref: D422Lot1.WEnR.2.4.1
Official reference number service contract: 2017/C3S_422_Lot1_WEnR/SC2
History of Modifications
Acronyms
Scope of the document
The scope of this document is to provide an overview of the product characteristics. The product covered here is the estimates of vegetation actual and potential evapotranspiration (ET,PET). The estimates of ET,PET is achieved by combining meteorological products from CDS ERA5 with Copernicus Global Land Service CGLS-based inputs consisting of land cover type, surface albedo (ALB), vegetation fractional cover (FC) and leaf area index (LAI) .
Executive summary
Accurate estimations of actual crop evapotranspiration (ET) give insight in water consumption, crop water stress, and production levels of crops. Accurate estimations of ET can also be used for optimal irrigation scheduling. ET is obtained by combining meteorological products from Climate Data Store (CDS) with Earth Observation based inputs consisting of land cover type, albedo, fractional cover (Fcover) and the leaf area index (LAI).The dataset presented here contains the MODGroETa model output which is actual and potential vegetation and soil evapotranspiration (ET) in millimeters with dekadal temporal resolution and 0.1-degree spatial resolution. The ET data available in this product is from 2000 to current with a lag time of two weeks. ET estimation is derived from daily ERA-INTERIM meteorological reanalysis data along with CGLS dekadal remotely sensed data products. In the near future it will switch to the AgERA5 meteorological data when it becomes available through the CDS. The theoretical base of the modeling approach is the Penman Monteith equation (Monteith, 1965). The MODGroETa model algorithm runs on a daily time scale that is then aggregated to dekadal, daily ET is the sum of ET from daytime and night. Vertically, ET is the sum of water vapor fluxes from soil evaporation, wet canopy evaporation and plant transpiration at dry canopy surface.
Figure 1 shows a sample output of the model representing the global dekadal actual evapotranspiration at 0.1-degree spatial resolution and aggregated over the period 08/20/2004 - 08/31/2004.
Figure 1: global dekadal aggregated (for the period from 8/20/2004 to 8/31/2004) actual evapotranspiration at 0.1 degree lat/lon grid.
Product description
MODGroETa version 1
Introduction
Remote sensing has been used as a feasible means to estimate regional evapotranspiration (ET) due to its spectral, spatial, and temporal characteristics. Remotely sensed data obtained from satellites provides continuous near-real time data that is useful in the monitoring of land surface biophysical variables that impacts ET.
Implementation of the MODGroETa code satisfies the need to generate near real time (NRT) global evapotranspiration.
The MODGroETa is a remote sensing-based evapotranspiration model developed by Gro- Intelligence using the same theoretical basis of MODIS Global Evapotranspiration (MOD16) that was developed by the National Aeronautics and Space Administration (NASA). The MOD16 evapotranspiration project was initiated by NASA's Earth Observing System (EOS) and implemented by the Numerical Terradynamic Simulation Group (NTSG) from the University of Montana to estimate global terrestrial evapotranspiration from land surfaces by using satellite remote sensing data. The MOD16 model uses a combination of meteorological and remote sensing ground surface data and is based on the Penman-Monteith equation. The MODGroETa model (Gro's version of MOD16) uses two sets of input data from the Climate Data Store (CDS),the first set is daily meteorological reanalysis data such as air temperature and solar radiation obtained from European Centre for Medium-Range Weather Forecasts (ECMWF) and the second set is Copernicus Global Land Service (CGLS) remotely sensed data products such as vegetation fractional cover (FC), albedo (ALB), and leaf area index (LAI).
Geophysical product description
This dataset contains the MODGroETa model output which is actual and potential vegetation and soil evapotranspiration (ET) in millimeters with dekadal temporal resolution and 0.1-degree spatial resolution. The ET data available in this product is from 1998 to current with a lag time of two weeks. ET estimation is derived from daily ERA-INTERIM meteorological reanalysis data along with CGLS dekadal remotely sensed data products. In the near future it will switch to the AgERA5 meteorological data when it becomes available through the CDS. The theoretical base of the modeling approach is the Penman Monteith equation. The MODGroETa model algorithm runs on a daily time scale that is then aggregated to dekadal, daily ET is the sum of ET from daytime and night. Vertically, ET is the sum of water vapor fluxes from soil evaporation, wet canopy evaporation and plant transpiration at dry canopy surface.
Product target requirements
The MODGroETa model requires both satellite products from the Copernicus Global Land Service (GLS) and meteorological data from the Climate Data Store (CDS). The required CDS product are operationally available (FC, LAI, Alb, land cover) in the Copernicus GLS (https://land.copernicus.eu/global/
). For the meteorological products the agriculturally tailored ERA5 product (AgERA5 - as developed in C3S) would be the most suitable data product to apply.
For many users in the agricultural community also assessments of crop water use and especially water stress for the running cropping season are highly relevant. This is especially true for the agro- business and water management communities, as early indications of production anomalies are of paramount importance for tax/subsidies and price volatility, and water stress indicators might warrant action with respect to water allocation and irrigation.
Therefore, ideally this product needs processing in near real time, i.e. updates are required each time the GLS product is update and then processed with the latest AgERA5t data.
Product Gap analysis
The MODGroETa model uses satellite products from the Copernicus Global Land Service (GLS) and meteorological data from the Climate Data Store (CDS) so the availability of the model product ET/PET and the gaps too depends on the input data availability from CDS and CGLS.
The MODGroET indicators should ideally also be available in near real time, and thus be updated every 10 days as soon as the required input data in the Copernicus GLS and CDS (EO data and AgERA5T, respectively) comes.
Data usage information
Practical usage considerations use of products
Accurate estimations of actual crop evapotranspiration (ET) give insight into water consumption, crop water stress, and production levels of crops. Accurate estimations of ET can also be used for optimal irrigation scheduling. ET is obtained by combining meteorological products from Climate Data Store (CDS) with Earth Observation based inputs consisting of land cover type, albedo, Fcover and the leaf area index (LAI).
Some of the direct application of the data presented are:
- understand how much water is consumed by plants over a period of time.
- as indicator for water stress
- to estimate irrigation needs
Geospatial coverage and dimensions, time coverage, dimensions
- Global scale
- Historic evapotranspiration and Near real time (NRT) evapotranspiration
- Resolution 10 km
The product will allow a quick comparison of changes in water use and drought in time and over different areas for specific crops. This can be used for improved crop and irrigation management and other water resources management applications.
Known Limitations of product
Algorithm Uncertainties
Many factors affect uncertainties in estimating ET , the following outlines key factors: (1) errors in land cover estimates as LAI/FPAR tends to be higher than actual ground conditions; (2) ground measurement errors as eddy covariance towers usually underestimated ET at high values with overestimation at the low values; (3) scaling from tower to landscape errors during validation as eddy covariance with small footprint (located at 2-5m) compared to ET estimates at 3x3 1km2 with potential significant heterogeneity; (4) algorithm assumptions and limitations since stomatal closure is assumed at night when many studies have reported the contrary for many plant species for a range of habitats. Moreover, increasing CO2 which tends to reduce plant transpiration is not accounted for in this simulation.
See the ATBD documentation with further details on product validation.
Concluding remarks
Discussion: Model performance is largely impacted by two factors. The first, and the most dominant factor is the land classification layer, which is the key factor in determining all model coefficients that drive surface and aerodynamic resistance. The second most important set of factors for the model are LAI and FC. Resampling the LUT layer from 300 meters to 10km merges different land types and creates a new pixel that holds the code for the majority of the land cover type. The resampling of LAI and FC has less impact than LUT resampling on ET estimation accuracy since it is a mathematical averaging rather than applying a majority value as the case with LUT.
Conclusion:
The model performance in cropland and wetland /grass was better than the model performance over open shrubland or savanna, and the model performance over forested areas was acceptable. Misclassification in LUT leads to the selection of the wrong parameters for vapor pressure deficit (VPD) and minimum air temperature (Tmin) for stomatal (cs) and canopy (cc) conductance constraints, resulting in less accurate ET estimates. Model results are most accurate in the middle of the season where LAI and Fc are high. Other uncertainties in the MOD16 ET algorithm may also arise from: i) ECMWF re-analysis data, which are validated at the global scale, and may require more detailed analysis when used at regional scales; (ii) resampling of reanalysis data with spatial resolution of ∼75 km to 10 km, (iii) infilling missing and contaminated LAI and FC values with low quality data, and (iv) ground-based measurements by the eddy covariance system and in the tower footprint used to validate the model.
References
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